Exit Strategies and Safety Concerns for Machinery Occupants Following Ice Failure and Submersion
Bibliographic record
Abstract
BACKGROUND: Of all drownings, 5 to 11% occur in submerged vehicles. Winter road workers are at high risk for vehicle submersion because they drive heavy vehicles over ice. METHODS: A crane was used for repeated occupied and unoccupied submersions of a 5-ton truck/snow plow (N = 25) and a 1-ton truck/snow plow (N = 23); some data were compared to those from our previous study on passenger vehicles (Aviat Space Environ Med 2010; 81:779-84). RESULTS: The 1-ton truck sank faster than an intact car, while the 5-ton truck sank within 4 s. Four subjects could escape through the windows, doors, or roof hatch when the 1-ton truck was on the surface or submerged. Hatch exit took 2-3 times longer than windows/doors. Because the 5-ton truck sank so quickly, there was no opportunity to escape while on the surface. With windows open, exit through the window, door, or roof hatch could only occur after the cab was full of water. With windows closed, rapid pressure buildup imploded the windshield. Bulk and buoyancy of thermoprotective flotation clothing did not impede exit in any scenario. One to three 200-L sealed containers mounted to the front of the 1-ton truck increased the Floating Phase by approximately 20-40 s each. CONCLUSIONS: Results suggest that a heavy vehicle will sink before surface exit is possible. Occupants would, therefore, be forced to breath-hold and make an underwater exit through a window, door, or roof hatch. Front-mounted external flotation devices on a light truck increased floating time and the possibility of exit while still on the surface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".